In this paper we present an approach to robot arm control based on exploiting the dynamical properties of a simple neural network oscillator circuit coupled to the joints of an arm. The entrainment and input/output properties of the oscillators are used to perform a variety of tasks with the same architecture, without any modeling of the arm or its environment. The approach is implemented on tw...

Locomotion, one of the most basic robotic functions, has been widely studied for several types of robots. As for self-reconfigurable modular robots, there are two types of locomotion; one type is realized as a series of self-reconfiguration and the other is realized as a whole body motion such as walking and crawling. Even for the latter type of locomotion, designing control method is more diff...

A new model for a neurodynamical classifier is proposed. The classifier is viewed as a generalized bi-directional associative memory (GBAM) [11] and is described in the language of differential geometry [12-14]. GBAM is a tensor-field system resembling a two-phase biological neural oscillator in which an excitatory neural field excites an inhibitory neural field, which reciprocally inhibits the...

The dynamical behaviour of the forced escape oscillator, which depends on the parameter values we considered, have been studied numerically using the techniques of phase portraits and Poincar'{e} sections. Also, we employed perturbation methods such as Lindstedt's method to obtain the frequency-amplitude relation of escape oscillator.

This work presents an optimization system developed to find optimal parameters for neural oscillators used for trajectory generation of an exoskeleton for lower limbs. An exoskeleton can be considered as a biped robot, with a given force interaction between the user and the robot. Since biped robots must present cyclical joint trajectories during the walking, neural oscillators are being used a...